04 - 05 July, 2018 | Hilton London Kensington, London, United Kingdom

Agenda Day 1

8:00 am - 8:30 am Registration and Coffee

8:30 am - 8:35 am Chairperson's Opening Remarks & Welcome

8:35 am - 9:00 am What are Your Event Objectives?

In order to boost networking and interactivity, the conference will begin with an opportunity for everybody to get to know one another. Attendees will then have 20 minutes to discuss their key objectives for attending the event which will be documented and used as a tool to influence discussions throughout the entire conference.

9:00 am - 9:40 am AI Journey: Building AI Capabilities within the Enterprise

Bas Geerdink - Technology Lead ING
  • Getting started with AI in the enterprise: Top-down strategy execution and bottom-up proof of concepts
  • Driving innovation in a financial enterprise: Create a data-driven FinTech mind-set and start-up way of working
  • Understanding how to move from a pilot project into a full scale deployment
  • Selecting the right architecture and technology to support you on your journey
  • Case studies: Exploring various AI and ML projects

Bas Geerdink

Technology Lead
ING

  • How mature is the industry relating to AI and Machine learning – pilot projects, live projects, evaluation, ROI?
  • What are the existing barriers for adoption and what can the industry do to overcome them?
  • Identifying the need: Ensuring that AI and ML projects are aligned to overall business objectives
  • Should you invest in these technologies? Discussing the value proposition and perceived ROI from machine learning and artificial intelligence?
  • How can we change organisational behaviour and accelerate trust and engagement with adoption of AI and ML capability
  • Allaying fears and creating AI advocates that can promote the future adoption of AI
  • How can we get more from our technology partners and ensure that we identify, select and implement the right technology
  • What does the future hold?

Bas Geerdink

Technology Lead
ING

Juan Amador

Global Head Financial Crime Risk Technology Strategy
HSBC

Karthik Rajaraman


Financial Services Data Management Professional

Simon Adams

Strategic Data Management Director
Towergate Insurance

Stephen Magora

Director, Data Analytics
Credit Suisse

10:40 am - 11:10 am How Automated Machine Learning Makes it Faster to Validate and Implement AI Solutions

Seph Mard - Head of Model Risk Management DataRobot

Seph Mard

Head of Model Risk Management
DataRobot

11:10 am - 11:40 am Morning Coffee and Networking Break

11:40 am - 12:20 pm Customer Insights & Analytics: Leveraging Data to Improve Experience, Loyalty and Engagement

Orlando Machado - Director, Customer Analytics and Data Science Aviva
  • Developing a data-driven strategy that is customer centric and improves engagement, experience and loyalty
  • Personalisation and intimacy: Leveraging insights to deepen customer relationships and align with their evolving expectations
  • Competitiveness: Predicting what the customer wants, creating personalised products and ensuring the right message is delivered at the right time

Orlando Machado

Director, Customer Analytics and Data Science
Aviva

12:20 pm - 12:50 pm Enterprise Data Management Platform: Ensuring Contextual, Always On, Real Time, Distributed and Scalable Solutions

Martin James - Regional Vice President, Northern Europe DataStax
  • Overview of DataStax: Who are we, our vision and reviewing our core solutions
  • The importance of customer centricity: Capturing and capitalizing on insights to develop personalized experiences
  • CARDS: Describing the 5 characteristics of Cloud Applications
  • Fraud detection and prevention solutions: AI and data innovation 

Martin James

Regional Vice President, Northern Europe
DataStax

12:50 pm - 1:50 pm Lunch and Networking Break

1:50 pm - 2:30 pm Intelligence-led Anti-Financial Crime

Juan Amador - Global Head Financial Crime Risk Technology Strategy HSBC
Summary.    Artificial Intelligence (AI) or Machine Learning (ML) is at the core of many recent developments in products and services.  With algorithms getting better at playing games or driving autonomous vehicles – managers everywhere have started asking whether the technology can be used to make better credit officers, doctors, customer service reps or even, skilled technology personnel.  This session will outline the work we have done to date and our plans for the next 2-3 years.     

Juan Amador

Global Head Financial Crime Risk Technology Strategy
HSBC

2:30 pm - 3:15 pm Quick Fire Technology Demonstrations

This is your opportunity to meet with the vendors in our exhibition area to learn more about the technology and services that are designed to support your projects. These tech demos will last approximately 10 minutes on four cycles with the opportunity to continue your discussion throughout the networking break.

3:15 pm - 3:30 pm Make Data Great Again: Creating a Solid Foundation for your Machine Learning

Andrew Carr - Data Engineering Lead Scott Logic
  • Understanding the impact of Data Quality in ML projects
  • How much poor quality data can you allow in training data before the ML model becomes unusable - how wrong can it really get? Answer : very
  • What do you do to improve data quality - how can you make data great again - hints on techniques, tools and approaches

Andrew Carr

Data Engineering Lead
Scott Logic

3:30 pm - 4:00 pm Afternoon Tea & Networking Break

4:15 pm - 4:55 pm Data Analytics within the Regulatory Space

Rahul Pal - Technology Consultant Bank of England
  • Overview of the Bank of England: Current focus, unique challenges and ongoing journey
  • Identifying and rationalising the different analytical challenges faced at a macro and micro level
  • Developing harmonised reporting methods that streamline and improve outcomes for the stakeholders
  • Rationalising technology strategy that supports business challenges and advances data analytics: Including systems, techniques such as ML etc.

Rahul Pal

Technology Consultant
Bank of England

4:55 pm - 5:35 pm Machine Learning, Big Data & Analytics to Combat Fraud and Financial Crime

Steve Jackson - Head of Financial Crime & MLRO Covea Insurance
  • Describing how advanced machine learning analytics can greatly reduce fraud, improve customer experience and drive competitive edge
  • Creating and utilizing insights to improve predictions on new types of fraud as well as measure and manage risk
  • Using AI and machine learning to improve speed and accuracy of data-driven decisions: Automating tasks to free up valuable resources
  • Choosing the right solutions: Understanding your unique business problems and only selecting solutions that achieve those business objectives

Steve Jackson

Head of Financial Crime & MLRO
Covea Insurance

5:35 pm - 5:40 pm Chairperson’s Closing Remarks and End of Conference Day One

5:40 pm - 6:40 pm Networking Drinks